As healthcare continues its march towards greater healthcare service transparency, the implementation of the Transparency in Coverage Rule has required payers to make available contract rates for public consumption in the complex Machine-Readable Files (MRFs). These files, intended to clarify healthcare pricing, have instead incorporated a myriad of 'ghost' or 'junk' rates due to standardization requirements, presenting both challenges and opportunities for healthcare data analysts and healthcare professionals.

The Payer MRFs list contracted rates for procedures that certain providers would never perform under normal circumstances. Is there real value in finding a rate for:

• Chiropractor performing a knee replacement.

• Dermatologist conducting heart surgery.

• Pediatricians offering joint replacements.

There are clear mismatches in provider capability and procedure. This is just the tip of the iceberg. Within the Payer MRF files, these examples are compounding the complexity and confusion for those trying to get value out of the Payer MRFs.

This issue arises because payers are required to provide contracted rates across all providers, both professional and institutional, resulting in significant duplicative data and a rate for every procedure for every provider, without regard to practical applicability. While this method ensures comprehensive data, it necessitates a discerning eye to identify and dismiss rates irrelevant to actual clinical practice.

Moreover, the complexity deepens with billing practices. Many service providers bill under a different provider or through a provider group NPI (National Provider Identifier), which are not always aligned with the rates listed in the MRFs. For analysts and industry professionals focused on the rates that are genuinely billed and reimbursed, tracing back to the billing NPI is crucial, but not possible within the Payer MRFs alone.

For those of us diving into these files for actionable insights, it's critical to approach the data with a nuanced understanding of its limitations. Dissecting the 'junk' rates not only helps in cleaning up data sets but also in refining analytics for more accurate reflections of the healthcare pricing landscape.

Deerhold's Innovative Approach:

At Deerhold, we tackle these challenges head-on by incorporating 837 data from over 1.2 million billing providers with the Payer MRF data resulting in a meaningful dataset that our customer can access through our PRIZM Rates product. This unique approach ensures that our customers receive contract rate data for procedures that providers actually perform and have billed for previously. With decades of experience in the healthcare space, our team at Deerhold understands big healthcare data and excels in delivering actionable insights efficiently.

Contact @Pete Titas or @Scott MacEwen for an overview and demo of PRIZM Rates.